This chapter contains sections titled: Organizing Committee, Motivations and Goals, Contributions, Proceedings

Verification, Model Checking and Abstract Interpretation

Non-terrestrial networks (NTNs) traditionally had certain limited applications. However, the recent technological advancements opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting its relevance to 5G networks and essentially, how it will play a pivotal role in the development of 6G and beyond wireless networks. The survey discusses important features of NTNs integration into TNs by delving into the new range of services and use cases, various architectures, and new approaches being adopted to develop a new wireless ecosystem. Our survey includes the major progresses and outcomes from academic research as well as industrial efforts. We first start with introducing the relevant 5G use cases and general integration challenges such as handover and deployment difficulties. Then, we review the NTNs operations in mmWave and their potential for the internet of things (IoT). Further, we discuss the significance of mobile edge computing (MEC) and machine learning (ML) in NTNs by reviewing the relevant research works. Furthermore, we also discuss the corresponding higher layer advancements and relevant field trials/prototyping at both academic and industrial levels. Finally, we identify and review 6G and beyond application scenarios, novel architectures, technological enablers, and higher layer aspects pertinent to NTNs integration.

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey.

Efforts towards hosting safety-critical, real-time applications on multicore platforms have been stymied by a problem dubbed the "one-out-of-m" problem: due to excessive analysis pessimism, the overall capacity of an m-core platform can easily be reduced to roughly just one core. The predominant approach for addressing this problem introduces hardware-isolation techniques that ameliorate contention experienced by tasks when accessing shared hardware components, such as DRAM memory or caches. Unfortunately, in work on such techniques, the operating system (OS), which is a key source of potential interference, has been largely ignored. Most real-time OSs do facilitate the use of a coarse-grained partitioning strategy to separate the OS from user-level tasks. However, such a strategy by itself fails to address any data sharing between the OS and tasks, such as when OS services are required for interprocess communication (IPC) or I/O. This paper presents techniques for lessening the impacts of such sharing, specifically in the context of MC2, a hardware-isolation framework designed for mixed-criticality systems. Additionally, it presents the results from micro-benchmark experiments and a large-scale schedulability study conducted to evaluate the efficacy of the proposed techniques and to elucidate sharing vs. isolation tradeoffs involving the OS. This is the first paper to systematically consider such tradeoffs and consequent impacts of OS-induced sharing on the one-out-of-m problem.

https://dl.acm.org/doi/pdf/10.1145/3273905.3273911

Supporting I/O and IPC via Fine-Grained OS Isolation for Mixed-Criticality Real-Time Tasks

Modern computing systems are often formed by multiple components that interact with each other through the use of shared resources (e.g., CPU, network bandwidth, storage). In this paper, we consider a representative scenario of one such system in the context of an Internet of Things application. The system consists of a network of self-adaptive cameras that share a communication channel, transmitting streams of frames to a central node. The cameras can modify a quality parameter to adapt the amount of information encoded and to affect their bandwidth requirements and usage. A critical design choice for such a system is scheduling channel access, i.e., how to determine the amount of channel capacity that should be used by each of the cameras at any point in time. Two main issues have to be considered for the choice of a bandwidth allocation scheme: (i) camera adaptation and network access scheduling may interfere with one another, (ii) bandwidth distribution should be triggered only when necessary, to limit additional overhead. This paper proposes the first formally verified event-triggered adaptation scheme for bandwidth allocation, designed to minimize additional overhead in the network. Desired properties of the system are verified using model checking. The paper also describes experimental results obtained with an implementation of the scheme.

/pdf/event-driven-bandwidth-allocation-with-formal-guarantees-for-4jxo0iwm5x.pdf

Event-Driven Bandwidth Allocation with Formal Guarantees for Camera Networks

The applicability of Simultaneous Multithreading (SMT) to real-time systems has been hampered by the difficulty of obtaining reliable execution costs in an SMT-enabled system This problem is addressed by introducing a scheduling framework, called CERT-MT, that combines scheduling-aware timing analysis with a cyclic-executive scheduler in a way that minimizes SMT-related timing variations The proposed scheduling-aware timing analysis is based on maximum observed execution times and accounts for the uncertainty inherent in measurement-based timing analysis The timing analysis is found to work for tasks with and without SMT, though some adjustments are required in the former case A large-scale schedulability study is presented that shows CERT-MT can schedule systems with total utilizations approaching 14 times the core count, without sacrificing safety

https://drops.dagstuhl.de/opus/volltexte/2020/12377/pdf/LIPIcs-ECRTS-2020-14.pdf/

Simultaneous Multithreading and Hard Real Time: Can It Be Safe?

Time-critical networks require strict delay bounds on the transmission time of packets from source to destination. Routes for transmissions are usually statically determined, using knowledge about worst-case transmission times between nodes. This is generally a conservative method, that guarantees transmission times but does not provide any optimization for the typical case. In real networks, the typical delays vary from those considered during static route planning. The challenge in such a scenario is to minimize the total delay from a source to a destination node, while adhering to the timing constraints. For known typical and worst-case delays, an algorithm was presented to (statically) determine the policy to be followed during the packet transmission in terms of edge choices. In this paper we relax the assumption of knowing the typical delay, and we assume only worst-case bounds are available. We present a reinforcement learning solution to obtain optimal routing paths from a source to a destination when the typical transmission time is stochastic and unknown. Our reinforcement learning policy is based on the observation of the state-space during each packet transmission and on adaptation for future packets to congestion and unpredictable circumstances in the network. We ensure that our policy only makes safe routing decisions, thus never violating pre-determined timing constraints. We conduct experiments to evaluate the routing in a congested network and in a network where the typical delays have a large variance. Finally, we analyze the application of the algorithm to large randomly generated networks.

/pdf/adaptive-routing-with-guaranteed-delay-bounds-using-safe-dqwidf7ob6.pdf

Adaptive Routing with Guaranteed Delay Bounds using Safe Reinforcement Learning

Devices sharing a network compete for bandwidth, being able to transmit only a limited amount of data. This is for example the case with a network of cameras, that should record and transmit video streams to a monitor node for video surveillance. Adaptive cameras can reduce the quality of their video, thereby increasing the frame compression, to limit network congestion. In this paper, we exploit our experience with computing capacity allocation to design and implement a network bandwidth allocation strategy based on game theory, that accommodates multiple adaptive streams with convergence guarantees. We conduct some experiments with our implementation and discuss the results, together with some conclusions and future challenges.

/pdf/game-theoretic-network-bandwidth-distribution-for-self-blxgf7gowr.pdf

Game-theoretic network bandwidth distribution for self-adaptive cameras

The ever increasing number of connected devices has lead to a metoric rise in the amount data to be processed. This has caused computation to be moved to the edge of the cloud increasing the importance of efficiency in the whole of cloud. The use of this fog computing for time-critical control applications is on the rise and requires robust guarantees on transmission times of the packets in the network while reducing total transmission times of the various packets.We consider networks in which the transmission times that may vary due to mobility of devices, congestion and similar artifacts. We assume knowledge of the worst case tranmssion times over each link and evaluate the typical tranmssion times through exploration. We present the use of reinforcement learning to find optimal paths through the network while never violating preset deadlines. We show that with appropriate domain knowledge, using popular reinforcement learning techniques is a promising prospect even in time-critical applications. (Less)

https://drops.dagstuhl.de/opus/volltexte/2020/12000/pdf/OASIcs-Fog-IoT-2020-6.pdf/

Routing using Safe Reinforcement Learning

Modern computer systems consist of large number of entities connected through a shared resource. One such system is a video surveillance network consisting of a set of cameras and a network manager...

https://www.tandfonline.com/doi/pdf/10.1080/23335777.2021.1887364

Gautham Nayak Seetanadi

Papers

Event-Driven Bandwidth Allocation with Formal Guarantees for Camera Networks

Adaptive Routing with Guaranteed Delay Bounds using Safe Reinforcement Learning

Game-theoretic network bandwidth distribution for self-adaptive cameras

Routing using Safe Reinforcement Learning

Control-based event-driven bandwidth allocation scheme for video-surveillance systems